1. Field of the Invention
The present invention relates to processing of global positioning systems (GPS) signals. In particular, the present invention relates to processing GPS signals in the presence of dynamically changing, false observations and multipath signal interference.
2. Discussion of the Related Art
Conventional GPS navigation may use a Kalman filter to improve accuracy in position determination using pseudorange and other measurements. This approach is based on a single mathematical model of the GPS receiver and its environment. Often, such a model assumes both a relatively constant velocity for the GPS receiver, and a static measurement environment. In practice, such assumptions are often found to be invalid. For example, the GPS receiver may be moving at constant velocity only for very limited time periods, with time periods of acceleration and deceleration in between. A GPS receiver also often changes its direction of travel. The appropriate model for each of these circumstances may be different. Transitions from one model to another model are also important. Thus, navigation based on a single Kalman filter is at best a compromise, and results in poor navigation performance.
In conventional GPS processing, multipath effects (i.e., the super-positioning of the direct line-of-sight signal path and indirect signal paths) are often present, especially in an urban canyon environment. From the Kalman's filter point of view, multipath effects affect the statistics in the measurement environment, thereby changes measurement accuracy (e.g., increasing measurement noise variance). Multi-path effects are typically time-varying, intermitten and random. In such an environment, a static multipath model is inappropriate. In the presence of multipath effects, the measurement error is likely positively biased, with an increased variance relative to the case in which multipath effects are not present. A Kalman filter designed for an environment without multipath effects does not perform well in the presence of multipath effects.
Further, multipath effects and other acquisition errors often cause severe noise in the measured GPS parameters, rendering such measurements unsuitable for use in the Kalman filter. These invalid measurements may lead to very large navigation error, thus significantly degrading the Kalman filter's performance. A complicated filtering of the measured parameters and noise-rejection before the measurements can be used in a Kalman filter. However, proper criteria for accepting or rejecting the measurements are not always clear or known, so that a proper measurement may be improperly rejected, and a false measurement may be improperly accepted. Even with good acceptance and rejection criteria, different weights should be given to navigation data under different collection conditions. For example, one measurement found barely acceptable should not be given the same weight as a measurement found well within the region of acceptance.